Performance of magnetic resonance imaging-based prostate cancer risk calculators and decision strategies in two large European medical centres

Objectives: To compare the performance of currently available biopsy decision support tools incorporating magnetic resonance imaging (MRI) findings in predicting clinically significant prostate cancer (csPCa). Patients and Methods: We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search. Performance was assessed by calibration, discrimination, decision curve analysis (DCA) and numbers of biopsies avoided vs csPCa cases missed, before and after recalibration, at risk thresholds of 5%–20%. Results: A total of 940 men were included, 507 (54%) had csPCa. The median (interquartile range) age, prostate-specific antigen (PSA) level, and PSA density (PSAD) were 68 (63–72) years, 9 (7–15) ng/mL, and 0.20 (0.13–0.32) ng/mL2, respectively. In all, 18 multivariable risk calculators (MRI-RCs) and dichotomous biopsy decision strategies based on MRI findings and PSAD thresholds were assessed. The Van Leeuwen model and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) had the best discriminative ability (area under the receiver operating characteristic curve 0.86) of the MRI-RCs that could be assessed in the whole cohort. DCA showed the highest clinical utility for the Van Leeuwen model, followed by the RPCRC. At the 10% threshold the Van Leeuwen model would avoid 22% of biopsies, missing 1.8% of csPCa, whilst the RPCRC would avoid 20% of biopsies, missing 2.6% of csPCas. These multivariable models outperformed all dichotomous decision strategies based only on MRI-findings and PSAD. Conclusions: Even in this high-risk cohort, biopsy decision support tools would avoid many prostate biopsies, whilst missing very few csPCa cases. The Van Leeuwen model had the highest clinical utility, followed by the RPCRC. These multivariable MRI-RCs outperformed and should be favoured over decision strategies based only on MRI and PSAD.</p

The development and characterisation of a bacterial artificial chromosome library for Fragaria vesca

<p>Abstract</p> <p>Background</p> <p>The cultivated strawberry <it>Fragaria ×ananassa </it>is one of the most economically-important soft-fruit species. Few structural genomic resources have been reported for <it>Fragaria </it>and there exists an urgent need for the development of physical mapping resources for the genus. The first stage in the development of a physical map for <it>Fragaria </it>is the construction and characterisation of a high molecular weight bacterial artificial chromosome (BAC) library.</p> <p>Methods</p> <p>A BAC library, consisting of 18,432 clones was constructed from <it>Fragaria vesca </it>f. <it>semperflorens </it>accession 'Ali Baba'. BAC DNA from individual library clones was pooled to create a PCR-based screening assay for the library, whereby individual clones could be identified with just 34 PCR reactions. These pools were used to screen the BAC library and anchor individual clones to the diploid <it>Fragaria </it>reference map (FV×FN).</p> <p>Findings</p> <p>Clones from the BAC library developed contained an average insert size of 85 kb, representing over seven genome equivalents. The pools and superpools developed were used to identify a set of BAC clones containing 70 molecular markers previously mapped to the diploid <it>Fragaria </it>FV×FN reference map. The number of positive colonies identified for each marker suggests the library represents between 4× and 10× coverage of the diploid <it>Fragaria </it>genome, which is in accordance with the estimate of library coverage based on average insert size.</p> <p>Conclusion</p> <p>This BAC library will be used for the construction of a physical map for <it>F. vesca </it>and the superpools will permit physical anchoring of molecular markers using PCR.</p

Comparison of Magnetic Resonance Imaging-Based Risk Calculators to Predict Prostate Cancer Risk

Importance: Magnetic resonance imaging (MRI)-based risk calculators can replace or augment traditional prostate cancer (PCa) risk prediction tools. However, few data are available comparing performance of different MRI-based risk calculators in external cohorts across different countries or screening paradigms. Objective: To externally validate and compare MRI-based PCa risk calculators (Prospective Loyola University Multiparametric MRI [PLUM], UCLA [University of California, Los Angeles]-Cornell, Van Leeuwen, and Rotterdam Prostate Cancer Risk Calculator-MRI [RPCRC-MRI]) in cohorts from Europe and North America. Design, Setting, and Participants: This multi-institutional, external validation diagnostic study of 3 unique cohorts was performed from January 1, 2015, to December 31, 2022. Two cohorts from Europe and North America used MRI before biopsy, while a third cohort used an advanced serum biomarker, the Prostate Health Index (PHI), before MRI or biopsy. Participants included adult men without a PCa diagnosis receiving MRI before prostate biopsy. Interventions: Prostate MRI followed by prostate biopsy. Main Outcomes and Measures: The primary outcome was diagnosis of clinically significant PCa (grade group ≥2). Receiver operating characteristics for area under the curve (AUC) estimates, calibration plots, and decision curve analysis were evaluated. Results: A total of 2181 patients across the 3 cohorts were included, with a median age of 65 (IQR, 58-70) years and a median prostate-specific antigen level of 5.92 (IQR, 4.32-8.94) ng/mL. All models had good diagnostic discrimination in the European cohort, with AUCs of 0.90 for the PLUM (95% CI, 0.86-0.93), UCLA-Cornell (95% CI, 0.86-0.93), Van Leeuwen (95% CI, 0.87-0.93), and RPCRC-MRI (95% CI, 0.86-0.93) models. All models had good discrimination in the North American cohort, with an AUC of 0.85 (95% CI, 0.80-0.89) for PLUM and AUCs of 0.83 for the UCLA-Cornell (95% CI, 0.80-0.88), Van Leeuwen (95% CI, 0.79-0.88), and RPCRC-MRI (95% CI, 0.78-0.87) models, with somewhat better calibration for the RPCRC-MRI and PLUM models. In the PHI cohort, all models were prone to underestimate clinically significant PCa risk, with best calibration and discrimination for the UCLA-Cornell (AUC, 0.83 [95% CI, 0.81-0.85]) model, followed by the PLUM model (AUC, 0.82 [95% CI, 0.80-0.84]). The Van Leeuwen model was poorly calibrated in all 3 cohorts. On decision curve analysis, all models provided similar net benefit in the European cohort, with higher benefit for the PLUM and RPCRC-MRI models at a threshold greater than 22% in the North American cohort. The UCLA-Cornell model demonstrated highest net benefit in the PHI cohort. Conclusions and Relevance: In this external validation study of patients receiving MRI and prostate biopsy, the results support the use of the PLUM or RPCRC-MRI models in MRI-based screening pathways regardless of European or North American setting. However, tools specific to screening pathways incorporating advanced biomarkers as reflex tests are needed due to underprediction.</p

Major-effect candidate genes identified in cultivated strawberry (Fragaria × ananassa Duch.) for ellagic acid deoxyhexoside and pelargonidin-3-O-malonylglucoside biosynthesis, key polyphenolic compounds

Strawberries are rich in polyphenols which impart health benefits when metabolized by the gut microbiome, including anti-inflammatory, neuroprotective, and antiproliferative effects. In addition, polyphenolic anthocyanins contribute to the attractive color of strawberry fruits. However, the genetic basis of polyphenol biosynthesis has not been extensively studied in strawberry. In this investigation, ripe fruits from three cultivated strawberry populations were characterized for polyphenol content using HPLC-DAD-MSn and genotyped using the iStraw35k array. GWAS and QTL analyses identified genetic loci controlling polyphenol biosynthesis. QTL were identified on four chromosomes for pelargonidin-3-O-malonylglucoside, pelargonidin-3-O-acetylglucoside, cinnamoyl glucose, and ellagic acid deoxyhexoside biosynthesis. Presence/absence of ellagic acid deoxyhexoside and pelargonidin-3-O-malonylglucoside was found to be under the control of major gene loci on LG1X2 and LG6b, respectively, on the F. × ananassa linkage maps. Interrogation of gene predictions in the F. vesca reference genome sequence identified a single candidate gene for ellagic acid deoxyhexoside biosynthesis, while seven malonyltransferase genes were identified as candidates for pelargonidin-3-O-malonylglucoside biosynthesis. Homologous malonyltransferase genes were identified in the F. × ananassa ‘Camarosa’ genome sequence but the candidate for ellagic acid deoxyhexoside biosynthesis was absent from the ‘Camarosa’ sequence. This study demonstrated that polyphenol biosynthesis in strawberry is, in some cases,under simple genetic control, supporting previous observations of the presence or absence of these compounds in strawberry fruits. It has also shed light on the mechanisms controlling polyphenol biosynthesis and enhanced the knowledge of these biosynthesis pathways in strawberry. The above findings will facilitate breeding for strawberries enriched in compounds with beneficial health effects.publishedVersio

Reducing prostate biopsies and magnetic resonance imaging with prostate cancer risk stratification

Objectives: To recalibrate and validate the European Randomized Study of Screening for Prostate Cancer risk calculators (ERSPC RCs) 3/4 and the magnetic resonance imaging (MRI)-ERSPC-RCs to a contemporary Norwegian setting to reduce upfront prostate multiparametric MRI (mpMRI) and prostate biopsies. Patients and Methods: We retrospectively identified and entered all men who underwent prostate mpMRI and subsequent prostate biopsy between January 2016 and March 2017 in a Norwegian centre into a database. mpMRI was reported using PI-RADS v2.0 and clinically significant prostate cancer (csPCa) defined as Gleason ≥ 3 + 4. Probabilities of csPCa and any prostate cancer (PCa) on biopsy were calculated by the ERSPC RCs 3/4 and the MRI-ERSPC-RC and compared with biopsy results. RCs were then recalibrated to account for differences in prevalence between the development and current cohorts (if indicated), and calibration, discrimination and clinical usefulness assessed. Results: Three hundred and three patients were included. The MRI-ERSPC-RCs were perfectly calibrated to our cohort, although the ERSPC RCs 3/4 needed recalibration. Area under the receiver operating curve (AUC) for the ERSPC RCs 3/4 was 0.82 for the discrimination of csPCa and 0.77 for any PCa. The AUC for the MRI-ERSPC-RCs was 0.89 for csPCa and 0.85 for any PCa. Decision curve analysis showed clear net benefit for both the ERSPC RCs 3/4 (>2% risk of csPCa threshold to biopsy) and for the MRI-ERSPC-RCs (>1% risk of csPCa threshold), with a greater net benefit for the MRI-RCs. Using a >10% risk of csPCa or 20% risk of any PCa threshold for the ERSPC RCs 3/4, 15.5% of mpMRIs could be omitted, missing 0.8% of csPCa. Using the MRI-ERSPC-RCs, 23.4% of biopsies could be omitted with the same threshold, missing 0.8% of csPCa. Conclusion: The ERSPC RCs 3/4 and MRI-ERSPC-RCs can considerably reduce both upfront mpMRI and prostate biopsies with little risk of missing csPCa